The invention relates generally to the field of encoding and decoding content. More particularly, the invention relates to extracting and loading data to/from a buffer.
Media applications have been driving microprocessor development for more than a decade. In fact, most computing upgrades in recent years have been driven by media applications, predominantly within consumer segments, but also in enterprise segments for entertainment, enhanced education and communication purposes. Nevertheless, future media applications will require even higher computational requirements. As a result, tomorrow""s personal computer (PC) experiences will be even richer in audio-visual effects as well as being easier to use and more importantly, computing will merge with communications.
Accordingly, the display of images, as well as playback of audio and video have become increasingly popular applications for current computing devices. Unfortunately, the quantity of data required for these type of applications tends to be very large. As a result, increases in computational power, memory and disk storage, as well as network bandwidth, have facilitated the creation and use of larger and higher quality images as well as longer and higher quality audio and video. However, content utilized by these applications is generally stored in a compressed format to conserve storage space and to reduce transmission bandwidth.
As a result, applications which support content, such as audio and video, are subject to real playback constraints imposed by audio and video data. Content such as audio and video data is generally streamed, in which transmitted content is played as it is received. In order to enable streaming, data is compressed before transmission to overcome network bandwidth limitations and to meet playback requirements. As a result, audio and video must be decoded in real time, whether they are streamed over a network or stored in a local device. In addition, computational requirements increase as the audio quality and video resolution and frame size increase. Consequently, decompression of video is one of the most computationally demanding requirements of popular applications.
Unfortunately, there is currently an imbalance between decreasing of computational costs while the costs of bandwidth and quality of service components stays stable. It is foreseeable that such an imbalance will shape the future of applications. As a result, as an ever increasing class of new algorithms and applications will be aimed to trade off the computational complexity resulting in improved audio-video quality, smaller bit rates, easy to manage tools, etc. Accordingly, this imbalance will result in new compression standards, new processing algorithms and end-to-end application paradigm where many operations in various domains will be necessary to ensure the proper delivery of multimedia from encoding and encryption to transmission, post-processing and management.
Commonly used compression algorithms such as joint photographic experts group (JPEG) and JPEG 2000 image compression, as well as motion picture expert groups (MPEG), MPEG-1, MPEG-2 and MPEG-4 audio and video compression combine two techniques. First, data is processed with a transform and then quantitized. Compression is then achieved by moving perceptionally and significant data. This technique is called lossy because original data is not exactly recovered by decompression. Results of the first step are further compressed using an entropy encoding technique. Entropy encoding replaces original data symbols (the quantitized transforms coefficients in this case) with symbols whose length depends on the frequency of the currents of the original data symbols. The most common original data symbols are replaced with short entropy encode symbols and the least common are replaced with long entropy encode symbols. Consequently, the length of entropy encode symbols vary in their bit length.
Image decode steps are executed in reverse order of the encode steps. Entropy decoding precedes transformed decoding, etc. Unfortunately, there is little data parallelism in entropy decoding due to data dependencies that result from different symbol links. Various architecture instructions are generally effective for transform operations, but tend to be of little use for entropy decoding. However, as architecture instructions increase with larger registers and new instructions, the fraction of time required for entropy decoding and media playback applications increases. Accordingly, improvements in entropy decoding lag behind those for transform decoding.
Therefore, there remains a need to overcome one or more of the limitations in the above-described existing.